Conventional approaches to automated question detection may generally analyze each encountered sentence as a whole. These approaches may include looking for keywords and “n-grams” (e.g., specific groupings of n-words) at the beginning or end of a sentence, using machine learning classifiers or full parsing to produce a hierarchical tree of the syntactic structure of the sentence, and so forth. While these approaches may be satisfactory under certain circumstances, there remains considerable room for improvement. For example, treating each sentence as a whole may render these approaches error-prone and/or impractical for a wide variety of applications. More particularly, the traditional n-gram approach may be unable to account for either intervening words that are not predefined as part of the n-gram or words located in the middle of the sentence, and the hierarchical tree approach may be computationally expensive, resource heavy and slow. As a result, neither approach may be suitable for real-time and/or low power applications such as, for example, personal assistant (PA) applications running on handheld devices.